Abstract P4-05-17: Mammaglobin is a potent regulator of breast cancer processes leading to disease progression
Bibliographic record
Abstract
Abstract Metastasis is the major cause of death in women suffering from breast cancer. To provide a better understand breast cancer progression, we have studied the role of mammaglobine-1 (MGB1) gene in breast cancer pathogenesis. MGB1 has been extensively studied as a diagnostic biomarker due to its abundant expression in mammary cancer cells. Yet, MGB1's role in disease progression is still unknown. Our experimental results demonstrate for the first time that MGB1 in a pivotal regulator on breast cancer malignancy. More precisely, loss of MGB1 expression correlates with a decrease in proliferation, spheroid formation, migration, and invasion capacities of breast cancer cells. Concomitantly, we also observe that MGB1 expression activates pro-malignant signaling cascades such as MAPKs, focal adhesion kinase (FAK) and NFkB pathways. Moreover, MGB1 promote epithelial to mesenchymal (EMT) features which coincide with our findings. Our study provides the first evidence for MGB1 as regulator of breast cancer malignancy and disease progression. Citation Format: Roxann Guerrette, Nadia Picot, Gilles Robichaud. Mammaglobin is a potent regulator of breast cancer processes leading to disease progression [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-05-17.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.028 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".